الفهرس 
REVIEW OF LITERATUREOnly 14 pages are availabe for public view
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Abstract
• The evaluation of SQ is regarded as the cornerstone for sustaining and monitoring the sustainability of agricultural systems as reported Yao, R.-J., et al; (2013)
• GIS is a good tool for storing, restoring and processing a large amount of data needed to estimate and map different soil parameters. The most crucial stage in the evaluation of SQI is the creation of a spatial distribution map for soil attributes.
• The results of the SQI spatial model developed were accepted with the current situation in the study area and were nearly identical to the results of the MEDALUS method.
• It is very important to assess soil quality periodically to identify agricultural practices that cause soil growth, and crop productivity, and monitoring soil degradation.
• In conclusion, the spatial model proposed in this study could be a more accurate methodology for assessing the spatial distribution of soil quality by including soil characteristics physical, chemical, and microbiological as indicators related to SQ.
• Creating classes for soil quality can reduce agricultural management expenses, for example by, enhancing slightly moderate classes of soil that would require upgrading compared to low-quality soils.
• The results of this study showed that only in the case of treated wastewater and its long-term application, relevant changes in soil properties can be produced. The physical, chemical, and fertility properties of the soil, are influenced by irrigated treatment wastewater.
• In this study, the SQI of various agro-ecosystems in the Serapium forest was assessed using the physical, chemical, and microbiological characteristics of the soil. According to (Rangel- Peraza, J.G., et al., 2017) for the soil quality index in the serapium forest, require measurement of organic matter, clay, EC, available nitrogen, available phosphate, available potassium, and microbiological properties are the most important elements, According to (Aprisal et al., 2019), the soil quality index suffers when these variables have low values (Martinez-Salgado, M.; et al., 2010).
• In this study found high concentration of TCF and TBC for each agro-ecosystem in the study area, according to studies, treated wastewater effluent can significantly increase the amount of harmful bacteria in soils (Santamaria and Toranzos, 2003; Gerba and Smith, 2005). E. coli 0157:H7, Salmonella spp., Clostridium botulinum, Shigella spp., and Streptococcus are
examples of pathogenic bacteria (Santamaria and Toranzos, 2003; Gerba and Smith, 2005; Ibekwe et al., 2013; Ibekwe et al., 2016).
The highly recommended:
• Analyze the current availability of TWW for long-term use in agricultural irrigation – Assess the impacts of the long-term reuse of TWW on both the Soil quality and crop production.
• Apply modern technology to the study, such as modeling, remote sensing, and geographic information systems. These technologies include interactive maps and user interfaces that can assist decision-makers and stakeholders in selecting the best TWW implementation. Second, all relevant laws and rules should be followed while managing TWW reuse, guarantee the quality of TWW used for irrigation, the growing of the permitted crops, and.
• For decision-makers, executing the successful reuse of reclaimed water requires an accurate database of the generated and processed wastewater. Therefore, before operating the TWW on a broad scale, it is necessary to employ advanced technology (interactive maps, mobile apps, and decision support systems) to deliver correct information to both stakeholders and decision-makers.
Application of SWOT analysis to identify opportunities and threats that affect on soil irrigation and characteristics, as well as weaknesses (heavy metals pollution and microbial pollution) and strengths (organic matter and N, ect)
Fig. (60): SWOT analysis for soil irrigation with treated wastewater.
Summery
The current study was carried out on serapium forest, which is one of Egypt’s oldest plantation forests, having been created in 1998, and located in the eastern Egyptian desert.
The Serapium Forest about 16 kilometers south of Ismailia is situated near the City of Ismailia’s municipal wastewater treatment plants, from which the 210 ha plantation area is derived. The major goals of this study are to define soil quality, offer suggestions for choosing indicators, and evaluate soil quality to critically evaluate the effect of treated wastewater on soil quality and the importance of evaluating the soil quality index in achieving sustainability of soil quality irrigated with treated wastewater and agricultural sustainability under different agro-ecosystems. Based on the vegetation cover, the year of agriculture, and other environmental criteria, seven parts have been selected from the study area. Each part represents an agro-ecosystem as follows: A, B, C, D, E, F, and P, where P is a control soil. The years of afforestation in the study area of each agro-ecosystem A, B, C, D, E, and F were 2014, 2010, 2012, 2002, 2006, and 1998, respectively, whereas the agro-ecosystem P is the control soil, which is non-cultivated.
All soil samples analyzed in this study included chemical, physical, and biological properties to determine the SQ for Serapium forest soils under different agro-ecosystems.
Chemical analysis of the treated wastewater sample, which was taken from the source of the irrigation system in the study area, was carried out at the laboratory of the Analysis and Consultation Unit of Evaluation and Remediation of Hazardous Solid Wastes in the National Research Center.
Physiochemical analyses of soil samples, which were collected and transported to the laboratory in refrigerated bags, were examined within 24 hours of collection and analyzed at the laboratory of soil and water, Ain Shams University, Faculty of Agriculture.
Microbiological analyses of soil and treated wastewater samples, which were collected and transported to the laboratory in refrigerated bags, were examined within 24 hours of collection. Sample analyses were carried out at the Laboratory of Agricultural Microbiology Department at the National Research Center. The use of treated wastewater in agriculture is based on an annex to the Egyptian code 501/2015. And also from the additional burden and maximum pathogen, it is reported that total coliform 108-1010/gm. non-indication that the water is polluted by stool, fecal
coliforms were used as a fecal indicator in the bacteriological examination for water drinking, when fecal coliforms and E. coli are used as indicators for bacterial and fungal pathogens.
The stages to achieve the goals of this study included laboratory analysis of physical, chemical, and biological soil properties. Additionally, the Global Positioning System (GPS) was utilized for geo-referencing the collected samples from the field of each agro-ecosystem. Also, using a developed spatial distribution pattern ”Modell Builder” with RS, GIS, and physical, chemical, and biological soil parameters can predict soil quality. The results of the suggested model were related to the soil ability of the study area, which was affected by treated wastewater irrigation. The soil quality index was estimated in two methods and the closest method for application was selected, then was used this method in building a simulation model to predict the soil quality and sustainability of soil quality irrigated with treated wastewater. The first method was principal component analysis (PCA), using the IBM SPSS software version 25 for statistical analysis. The PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations from related variables to a set of numerical values that are not linearly related variables called Principal Components (PCs), also can be interpreted and understood as a process that occurs in the soil and have importance in agriculture soil.
Soil properties were summarized using PCA, and then the soil quality index for every agro- ecosystem in the study area was calculated by the equation of SQI. The second method is the MEDALUS technique, which is extremely adaptable, allowing for modifications to determine SQI for each agro-ecosystem based on local conditions and information availability. Accordingly, new parameters were introduced to the standard MEDALUS approach for soil irrigated with treated wastewater in the study area, and then the equation of soil quality index was applied to this method to predict SQ.
Based on the previous methods, the closest method was chosen to apply to the soil quality prediction model, which was the MEDALUS method, because it is simple and suitable for application in the simulation model. However, the selection of parameters associated with soil quality, which were used in the location study, was added based on the PCA by components matrix which shows the correlations between components estimated by principal components (PCs) analysis and the original variables.
In this study, the elements that related to the soil quality were comprised of the following fourteen indicators: soil pH, soil texture, available nutrients N, P, K, Mn, Zn, heavy metals: Cu, Fe, Cr, total bacteria and total fungi account. These indicators were selected according to the components matrix from the outputs of the PCA analysis by SPSS software version 25.
IDW interpolation in the ArcGIS software version 10.4.1, was used to map the spatial soil properties of each agro-ecosystem. The IDW approach was used to forecast the values of variables in un-sampled places. The stated assumption of interpolation utilizing (IDW) is that elements that are close to one another are more similar than those that are farther apart. IDW predicts a value for each un-estimated place in the study area using the estimated values close to the prediction location. Through the resulting maps of the spatial distribution for the chemical and biological characteristics of each agro-ecosystem.
Then the development of a spatial model for SQI assessment along with its validation using “Model Builder” in ArcGIS software version 10.4.1, which manages and extracts observations from spatial analysis data of each agro-ecosystem and predicts soil quality in each of them.
The following results were obtained for soil properties of agro-ecosystems in the study areas of serapium forest.
The mean values of EC for each agro-ecosystem ranged between 0.142 and 0.825 ds/m, whereas for agro-ecosystem P, which is non-cultivated, it was act as a control soil, it was 1.429 ds/m, indicating the soil of all agro-ecosystems in the study area is classified as non-saline according to FAO (USDA). May be the reason was time of sampling, where the highest precipitation was observed during October and November, where the samples were collected.
The mean results values of the pH for each agro-ecosystem were the following: 6.829, 7.215, 7.568, 7.470, 7.695, 7.490, and 8.13 for agro-ecosystems A, B, C, D, E, F, and P, respectively. The data obtained refers to the pH of agro-ecosystems A and B, which were neutral, but agro- ecosystems C, D, E, and F were slightly alkaline, while the pH mean value of agro-ecosystem P was moderately alkaline.
The mean values of the organic matter content percentage in soil for each agro-ecosystem were the following: 0.124, 0.127, 0.125, 0.125, 0.129, 0.17, and 0.112 for agro-ecosystems A, B, C, D, E, F, and P, respectively. Those results refer to a lower content of organic matter in all locations (< 0.9 %) for all agro-ecosystems. Might be explained by accelerated organic matter mineralization, which was enhanced by favorable climate factor conditions, especially the study area located in semi-arid area.
It was found the proportions of the major elements N, P, and K were close in the agro-ecosystems A, B, C, D, and E, where at a low level according to the FAO and some scientific papers published in international journals, however, in the agro-ecosystem F, the proportions were between moderate and high level, whereas agro-ecosystem P, was low level of major elements.
The results analyses of heavy metals Cu, Cr, Ni, and Zn refer to safe limits of heavy metal in soil. When compared to the Threshold and Permissible limits, with the mean concentration of Zn, Cr, Cu, and Ni in soil samples of each agro-ecosystem were below the threshold limits set. That could be due to the efficiency of heavy metal removal and indicating the high quality of the wastewater treatment process at the Serapium facility in Ismailia. Additionally, it’s possible that 25 to 50 percent of the entering biochemical oxygen demand (BOD), 50 to 70 percent of the total suspended solids (SS), and 65 percent of the oil and grease are removed during the initial treatment. Some organic nitrogen, organic phosphorus, and heavy metals linked with solids are also eliminated during primary sedimentation, while colloidal and dissolved constituents are unaffected according to information by the manager of serapium plant.
The mean results analyses of soil microbiology found that the total count of coliform bacteria exceeded the permissible limits according to the maximum additional disease burden. Similarly, total fungi were higher than the maximum limit.
Soil quality index using (PCA) for each agro-ecosystem.
The PCA method was utilized to estimate the SQI for soil irrigated with treated wastewater, per agro-ecosystem. Then, for each agro-ecosystem, was used an interpolation approach with the method IDW in the ArcGIS program to map SQI. The results estimating of SQI for agro- ecosystems A, B, C, D, E, F, and P were ranged (0.57-0.92), (0.58-0.76), (0.52-0.63), (0.59-0.92),
(0.56-0.72), (0.99-1), and (0.52-0.63), respectively. If the SQI value has been obtained, the soil quality can be determined according to (Aprisal et al., 2019) reported the following categories of soil quality: very good (0.8-1), good (0.6-0.79), moderate (0.35-0.59), and bad (0.20–0.34), and
very bad (0–0.19).
The MEDALUS Approach for Estimating SQI.
The selection of soil parameters, associated with soil quality, is achieved based on the PCA method by choosing the parameters that have the highest values in each column of the principal components (PCs) in the component matrix. Table of component matrix shows the correlations between components estimated by principal components (PCs) analysis and the original variables. Accordingly, fourteen soil parameters were considered in MEDALUS equation. These parameters include; EC, soil pH, OM, sand, N, P, K, Mn, Fe, Cr, Cu, Zn, TBC, TFC. Then applied equation of the SQI in MEDALUS method. The values of SQI in agro-ecosystems A, B, C, D, E, F, and P ranged from (1.36-1.48), (1.34- 1.48), (1.36 - 1.49), (1.38 - 1.54), (1.40 - 1.52), F (1.20 - 1.33),
and P (1.53 - 1.62), where SQ of the area samples for each agro-ecosystem. According to the standard MEDALUS approach, the following classification of soil quality: high quality is (<1.13), moderate quality ranged from 1.13 to 1.45, low quality is (> 1.45).
The development of a spatial model as a Simulation Model was based on the Model Builder tool in ArcGIS and equation for estimating SQI in MEDALUS method.
Model Builder was used to automate and document selected spatial analysis and data management procedures as a diagram of collected chains, which are a series of geo-processing tools that use one process’ result as the input to another. In order to obtain the final SQI map, the following steps were applied in this study:
1- Interpolation score for each soil parameter based on MEDELUS method (scoring range from 1 to 2).
2- Use map Algebra to produce the soil quality index through feeding Equation of SQI as following: (SQI= parameter 1 x parameter 2 x parameter3 x parameter n) 1/n . where n is number of parameter.
3- Raster layer for SQI for each agro-ecosystem, the final resulting raster assessed and displayed as soil quality map for each agro-ecosystem.
The maps obtained from the simulation model are similar to the maps of the MEDALUS method, where the range of SQI of the agro-ecosystems was nearly identical. The simulation model has been tested with agro-ecosystems A and B. The results were similar to the MEDALUS method as mentioned previously. To assess the SQI, the soil characteristics of the agro-ecosystems in the study area were chosen as the indicators that are related to soil quality. Based on the chosen parameters, physical, chemical, and biological, one can predict soil quality by the MEDALS approach using the ’Modell Builder’ spatial distribution pattern with RS and GIS, and then understanding the importance of assessing the SQI in achieving the sustainability of soil quality irrigated with treated wastewater and agricultural sustainability under different agro-ecosystems. The appraisal of SQ is considered as a backbone for observing and maintaining the sustainability of agricultural systems.
Therefore, SQ evaluation should be conducted with the least amount of subjectivity possible using a quantitative, documented, repeatable, and spatially explicit approach. Additionally, indicators that affect soil parameters are required for assessing soil quality. In assessing whether the soil is suitable for crop production, physical, chemical, and biological components are extremely important, which in turn affects soil quality and crop yields.